Associate Data Science

You will be part of NPCI’s Market Innovation team, working at the intersection of advanced machine learning, deep learning, graph AI, and Generative AI to build next-generation intelligent systems for India’s digital payments ecosystem.

This role focuses on solving India-scale problems such as fraud detection, mule/AML risk modeling, transaction intelligence, and conversational AI, using both classical ML and cutting-edge AI architectures (LLMs, GNNs, Transformers, Agentic AI systems).

You will design end-to-end AI systems—from problem formulation, feature engineering, and model development to GPU-accelerated optimization and production deployment, ensuring low latency, scalability, and robustness.

The role offers a unique opportunity to work on

• Graph-based fraud detection systems
• Agentic AI & LLM-powered platforms (RAG, MCP, workflows)
• GPU/CUDA optimized AI pipelines
• Privacy-preserving and federated AI systems

You will collaborate with top academic institutions (IITs/IISc) and cross-functional teams to push the boundaries of applied AI in financial systems.

Job Details

• Job Title: Data Scientist – AI Engineer
• Division: NPCI Data Analytics – Market Innovation
• Education: B.Tech / M.Tech / MSc / MCA (PhD preferred) in CS, AI, DS, Mathematics or related field
• Employment Type: Full-time
• Location: Hyderabad
• Role Type: Permanent

Key Responsibilities

Machine Learning & Advanced Modeling

• Develop and deploy ML/DL models (Logistic Regression, RF, XGBoost, NN, CNN, Transformers, GANs)
• Build models for fraud detection, AML, anomaly detection, transaction intelligence
• Work on imbalanced datasets using advanced sampling and cost-sensitive learning

Graph AI & Advanced Systems

• Design Graph AI models: GNN, GCN, GAT, temporal graph networks
• Apply network analytics for fraud rings, mule detection, behavioral risk signals

Generative AI & Agentic Systems

• Build LLM-powered applications (chatbots, complaint intelligence, document analysis)
• Implement
• RAG pipelines
• Agentic workflows & MCP (Model Context Protocols)
• Prompt engineering & LLM fine-tuning

Feature Engineering & Data Science

• Perform EDA, feature engineering (temporal, behavioral, aggregated features)
• Work with structured, semi-structured, and unstructured data

Model Optimization & GPU Acceleration

• Optimize models for
• Latency & throughput
• GPU performance (CUDA-based optimization)
• Use libraries such as
• RAPIDS, cuDF, cuML, cuGraph, PyTorch Geometric

Evaluation & Experimentation

• Design custom loss functions (weighted BCE, cost-sensitive)
• Apply business-aligned metrics:
• Precision@K, Recall, ROC-AUC, PR-AUC
• Use robust validation techniques (cross-validation, time-based splits)

Deployment & Production Systems

• Integrate models into batch and real-time production systems
• Design scalable ML pipelines & APIs
• Monitor
• Model drift
• Performance stability
• Business impact

Collaboration & Research

• Work with data engineers, product teams, and business stakeholders
• Contribute to research, innovation, and academic collaborations
• Stay updated on latest AI advancements (LLMs, Graph AI, Federated Learning)

Requirements

Required Technical Skills

Core ML & Data Science

• Strong in
• Supervised & unsupervised learning
• Statistical modeling (Logistic Regression, DA)
• Tree models (RF, XGBoost, LightGBM)
• Deep Learning
• NN, CNN, Transformers, GANs

Generative AI & LLM Stack

• Hands-on experience with:
• LLMs (OpenAI, open-source models)
• Prompt engineering, fine-tuning
• RAG pipelines & vector databases
• Agent frameworks & MCPs

Graph AI

• Experience with
• GNN, GCN, GAT
• Graph-based fraud detection
• Network analytics

Programming & Tools

• Strong proficiency in
• Python (NumPy, Pandas, scikit-learn)
• SQL (large-scale data processing)
• Frameworks
• PyTorch / TensorFlow
• PyTorch Geometric

Key Skills and Experience Required

• Strong foundation in
• Mathematics, probability, statistics
• Data structures & algorithms
• Expertise in
• Feature engineering & model evaluation
• Handling large-scale datasets
• Experience with
• Imbalanced datasets & sampling techniques
• Custom loss functions & business metrics
• Knowledge of
• Model deployment & production pipelines
• Model monitoring & performance tracking
• Strong
• Problem-solving ability
• Communication & stakeholder management
• Ability to translate business problems into scalable AI systems